lets_plot.geom_linerange

lets_plot.geom_linerange(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, **other_args)

Display a line range defined by an upper and lower value.

Parameters

  • mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

  • data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

  • stat (str, default=’identity’) – The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

  • position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

  • show_legend (bool, default=True) – False - do not show legend for this layer.

  • sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

  • tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

  • other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

Geom object specification.

Return type

LayerSpec

Note

geom_linerange() represents a vertical interval, defined by x, ymin, ymax.

geom_linerange() understands the following aesthetics mappings:

  • x : x-axis coordinates.

  • ymin : lower bound for line range.

  • ymax : upper bound for line range.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • size : lines width.

  • linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

Examples

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from lets_plot import *
LetsPlot.setup_html()
data = {
    'x': ['a', 'b', 'c', 'd'],
    'ymin': [5, 7, 3, 5],
    'ymax': [8, 11, 6, 9],
}
ggplot(data, aes(x='x')) + \
    geom_linerange(aes(ymin='ymin', ymax='ymax'))


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import numpy as np
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
n = 800
cat_list = {c: np.random.uniform(3) for c in 'abcdefgh'}
np.random.seed(42)
x = np.random.choice(list(cat_list.keys()), n)
y = np.array([cat_list[c] for c in x]) + np.random.normal(size=n)
df = pd.DataFrame({'x': x, 'y': y})
err_df = df.groupby('x').agg({'y': ['min', 'max']}).reset_index()
err_df.columns = ['x', 'ymin', 'ymax']
ggplot() + \
    geom_linerange(aes(x='x', ymin='ymin', ymax='ymax', fill='x'), \
                    data=err_df, show_legend=False, color='black', size=1) + \
    geom_point(aes(x='x', y='y'), data=df, size=4, alpha=.1, color='black', \
               tooltips=layer_tooltips().line('@y'))